Most of the time this math works quietly behind the scenes . . . until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

    At $29 and cheaper than a good pizza, you will get the book in its current partial form, along with all future chapters, updates, and fixes for free. As of the day I wrote this description, Code Bright had 130 pages and was just getting started. To give you some perspective on how detailed it is, Code Happy was 127 pages in its complete state. Want to know more? Carry on reading.Welcome back to Laravel. Last year I wrote a book about the Laravel PHP framework. It started as a collection of tutorials on my blog, and eventually became a full book. I definitely didn’t expect it to be as popular as it was. Code Happy has sold almost 3000 copies, and is considered to be one of the most valuable resourcesfor learning the Laravel framework.Code Bright is the spiritual successor to Code Happy. The framework has grown a lot in the past year, and has changed enough to merit a new title. With Code Bright I hope to improve on Code Happy with every way, my goal is, to once again, build the most comprehensive learning experience for the framework. Oh, and to still be funny. That’s very important to me.Laravel Code Bright will contain a complete learning experience for all of the framework’s features. The style of writing will make it approachable for beginners, and a wonderful reference resource for experienced developers alike.You see, people have told me that they enjoyed reading Code Happy, not only for its educational content, but for its humour, and for my down to earth writing style. This is very important to me. I like to write my books as if we were having a conversation in a bar.When I wrote Code Happy last year, I was simply a framework enthusiast. One of the first to share information about the framework. However, since then I have become a committed member of the core development team. Working directly with the framework author to make Laravel a wonderful experience for the developers of the world.One other important feature of both books, is that they are published while in progress. This means that the book is available in an incomplete state, but will grow over time into a complete title. All future updates will be provided for free.What this means is that I don’t have to worry about deadlines, or a fixed point of completion. It leads to less stress and better writing. If I think of a better way to explain something, I can go back and change it. In a sense, the book will never be completed. I can constantly add more information to it, until it becomes the perfect resource.Given that this time I am using the majority of my spare time to write the title (yes, I have a full time job too!), I have raised the price a little to justify my invested time. I was told by many of my past readers that they found the previous title very cheap for the resource that it grew into, so if you are worried about the new price, then let me remind you what you will get for your 29 bucks.The successor to Code Happy, seen by many as the #1 learning resource for the Laravel PHP framework.An unending source of information, chapters will be constantly added as needed until the book becomes a giant vault of framework knowledge.Comedy, and a little cheesy, but very friendly writing.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

    Describes how fractals were discovered, explains their unique properties, and discusses the mathematical foundation of fractals.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    "I like how the author builds quite high expectations in the introduction of the book, and manages to fulfill them fully throughout the book.""I am amazed at his ability to focus on teaching people, like me, who have done basically nothing with cameras other than point and shoot. There is a wealth of useful and understandable information in this book,...""I've been photographing for years since first SLR in 1960s and yet I found much of interest and some I was unfamiliar with. I especially appreciated the clear, concise presentation."" Even us 'know-it-alls' are surprised by the information that cleared-up some uncertain items that had existed too long.""As a former photography student who learned almost nothing over a two year period, I can now say that I fully understand the terminology and possess the technical skill to take better photographs and possibly revisit my dream of becoming a sports photographer." Whether You are New to Photography,making the transition from Film to Digital Photography, or just want to improve your skills, this book will save you TIME and MONEY! No more intimidation! If you understand how Digital Cameras work, every photo becomes a learning experience. Many people are walking around with expensive cameras that they barely know how to use and others are missing great photo opportunities because they have been misinformed by well-meaning friends. This book is based on the premise that a solid understanding of photographic equipment results in a faster learning curve and better photos. The technology is actually quite simple when it is broken down and clearly explained.Without an understanding of cameras, you are at the mercy of others in all your buying decisions. You will only scratch the surface of your camera's capabilities until you take on its manual settings. You need to read this book because: It will take the confusion out of Photographic terminology by clearly defining every important term used. Your learning will be enhanced by 120 charts and photos that will simplify discussions and help you to remember what you learned. You will learn why buying a camera based on Megapixels alone is a bad idea. You will learn how sensor size and lens selection affect image quality more than any other factors. Strange photographic terms will loose their mystique once you learn how they were carried over from film photography. ˃˃˃ You will also learn: How digital images are created and stored.Which file format to use when editing photos.Which file format to use when sharing photos.How to select a lens for your DSLR camera.How to properly adjust exposure.Why a zoom lens can be a very good investment.Why you should care about focal length, aperture, shutter speed, and ISO as well as what these terms mean. ˃˃˃ About the author: Al Judge is a professional photographer living in Sedona, Arizona. His works are displayed in several locations around town. He is a best selling author with more than a dozen photography books. Don’t waste any more time! Scroll up and grab a copy today for little more than the cost of a cup of coffee!

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

    Aimed at undergraduate students in mathematics, physics, and engineering, the book's intuitive explanations, lack ofadvanced prerequisites, and consciously user-friendly prose style will help students to master the subject more readily than was previously possible. The key to this is the book's use of new geometric arguments in place of the standard calculational ones. These geometric arguments are communicatedwith the aid of hundreds of diagrams of a standard seldom encountered in mathematical works. A new approach to a classical topic, this work will be of interest to students in mathematics, physics, and engineering, as well as to professionals in these fields.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    From the first lesson to the last, each chapter introduces games of increasing complexity and then teaches the game theoretical tools necessary to solve them. Inside, you will find: All the basics fully explained, including pure strategy Nash equilibrium, mixed strategy Nash equilibrium, the mixed strategy algorithm, how to calculate payoffs, strict dominance, weak dominance, iterated elimination of strictly dominated strategies, iterated elimination of weakly dominated strategies, and more! Dozens of games solved, including the prisoner's dilemma, stag hunt, matching pennies, zero sum games, battle of the sexes/Bach or Stravinsky, chicken/snowdrift, pure coordination, deadlock, and safety in numbers! Crystal clear, line-by-line calculations of every step, with more than 200 images so you don't miss a thing! Tons of applications: war, trade, game shows, and duopolistic competition. Quick, efficient, and to the point, Game Theory 101: The Basics is perfect for introductory game theory, intermediate microeconomics, and political science.